MemU-Net: A new volumetric dose prediction model using deep learning techniques in radiation treatment planning

Abstract

Objective:Automatic radiation treatment planning systems are gaining significance nowadays. This paper presents a novel deep learning architecture for automatically generating the radiotherapy dose information given to the cancer patients. Methods:A new deep learning architecture MemU-Net is proposed. MemU-Net incorporates the principles of two existing deep learning architectures: Memnet, which was proposed for image restoration, and U-Net, which was proposed for biomedical image segmentation. The proposed model is first trained and tested with the public dataset (OpenKBP). The predicted dose distributions are evaluated based on dosimetric values and Dose volume histogram measures. The results are compared with the predictions made by a standard 3D U-Net, Residual U-Net, Dense U-Net and 3D GAN. The statistical significance of the proposed model is tested by computing the paired t-test P-values. Finally the proposed model is trained and tested on a private dataset collected from MVR Cancer Center and Research Institute. The model performance was evaluated by plotting DVHs and analyzing dosimetric values. Results:The dose distributions of MemU-Net shows superiority in performance compared to other models. MemU-Net had a lower mean absolute error of the predicted and ground truth doses than other models for all targets and OARs except spinal cord. The dosimetric measures of the MemU-Net outperformed the state of the art models except in the case of D0.1cc of OARs. Conclusion:A new variant of U-Net, MemU-Net is proposed, that performs better than the state of the art models, in the dose prediction domain in radiotherapy.